Epileptogenic focus detection in intracranial EEG based on delay permutation entropy

Abstract

Epileptogenic localization is a critical factor for successful epilepsy surgery. Determining the epileptogenic hippocampus with single channel intracranial electroencephalography (iEEG) recording is beneficial to decrease the risk of infection compared with that based on multi-channel iEEGs. Delay permutation entropy (DPE) methodology is presented in this study to measure iEEG with different delay lag based on focal epileptogenic zone. A total of 1600 20-s epileptic iEEG are evaluated and are used as features to classify epileptogenic and non-epileptogenic zone. Experimental results show that the DPE index of epileptogenic iEEG is significant lower than that of non-epileptogenic hemisphere when delay lag ranges from 5 to 30 (p=0.01). In addition, the accuracy of identifying epileptogenic region with the DPE index is increased when the delay lag between 5 and 25, compared to the performance of the PE index.

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

“…The highest value of the MCC parameter for the Morlet wavelet kernel confirms the suitability of the Morlet wavelet kernel for the classification of the focal and non-focal EEG signals. The comparison of our proposed method with previously reported methods [65,66] using the same database is presented in Table 4. Our proposed method shows the highest classification accuracy of 87%.…”

“…In [65], delay permutation entropy (DPE) is used as the feature for the classification of focal and non-focal EEG signals. The maximum classification accuracy obtained using the SVM classifier for 50 focal and 50 non-focal EEG signals is 84%.…”

“…Classification Accuracy (%) DPE and SVM [65] 84 SpEn Avg , AVIF and LS-SVM [66] 85 Proposed method 87…”

Differentiating Interictal and Ictal States in Childhood Absence Epilepsy through Permutation Rényi Entropy

“…Ouyang et al [9] investigated by multiscale PE the transition of brain activities towards an absence seizure. Zhu et al [10] demonstrated, by experimental results, that PE is able to identify epileptic seizures in EEG and intracranial EEG (iEEG). The same authors proposed an unsupervised multi-scale K-means (MSK-means) algorithm to distinguish epileptic EEG signals and to identify epileptic zones [11].…”

Analysis of Epileptic EEG Signals with Simple Random Sampling J48 Algorithm

“…EEGs which record the voltage fluctuations resulting from ionic current flows within the neurons are capable of increasing insights into brain dysfunction and even of yielding information useful for diagnostic purposes [2]. Nowadays, it is widely used in the detection of epilepsy [3], [4] as well as characterization of sleep phenomena [5], encephalopathy [6] or Creutzfeldt-Jakob disease [7] and monitoring the depth of anesthesia [8] and the location of epileptic focus [9]. Automatic epileptic classification systems are the trend in both research and clinical areas because the traditional visual inspection of EEG signals requires highly trained medical professionals.…”